Open Source Computer Vision Research Articles

ГЕОДЕЗІЯ, КАРТОГРАФІЯ І АЕРОФОТОЗНІМАННЯ

The aim of the work is to develop an automated measuring system in a mechanical gyrocompass with the help of specially developed hardware and software in order to facilitate the operation of the device and minimize observer errors. The developed complex provides automation only for the time method, as for the method of the turning point it is necessary to constantly contact the motion screw of the total station. The project is based on an integrated system, the hardware part of which contains a single-board computer, camera, and lens. The main software is a developed motion recognition algorithm with the help of image processing. This algorithm was created using the Python programming language and the open-source computer vision library OpenCV. With the help of the hardware, a video image of the gyroscope's reference scale is obtained, and with the help of the software, the moving light indicator and its position relative to the scale are identified in this image. The result of the study is a functioning automatic measurement system, which determines the value of the azimuth of the direction with the same accuracy as manual measurements. The system is controlled remotely via a computer and wi-fi network. To test the system, a series of automatic and manual measurements were performed simultaneously at the same point for the same direction. Based on the results obtained, it can be stated that the accuracy of the system is within the limits specified by the manufacturer of the device for manual measurements. The application of computer vision technology, namely the tracking of a moving object in the image for gyroscopic measurements can give a significant impetus to the development of automation systems for a wide range of measuring instruments, which in turn can improve the accuracy of measurement results. The developed system can be used together with the Gyromax AK-2M gyrocompass of GeoMessTechnik for carrying out automated measurements, training of new operators. With the help of the developed model, it is possible to avoid gross errors of the observer, to facilitate the measurement process which will not demand the constant presence of the operator near the device. In some dangerous conditions, this is a significant advantage.

Facial Recognition in Public Areas

The security of information nowadays is very significant and difficult, so there are a number of ways to improve security. Especially in public areas like airports, railway stations, Universities, ATMs, etc. and security cameras are presently common in these areas. So, in this paper, we are presenting how Facial recognition can be used in public areas like airports, toll gates, offices, etc. We are comparing or matching a face of a person who we want to detect, with the video which is recorded through CCTV. There are certain algorithms to detect faces from video like through HAAR cascades, eigenface, fisher face, etc. open-source computer vision library is used for facial recognition.

Automatic Social Distance Monitoring system using Deep Learning Algorithms

In recent months, the worldwide outbreak of Corona Virus Disease (COVID-19) has created a panic situation for all human beings irrespective of the geographic location and economic status of the countries. The communal spread of the deadly virus can be prevented by maintaining the social distance between people and the wearing of face masks. This paper provides a prominent solution for monitoring using the Open-Source Computer Vision Library (Open-CV) and Deep Learning Algorithm to course people in public areas and to avoid crowding. This proposed project can be implemented using images taken from the Closed Circuit Television (CCTV). The distance between the people is detected with help of an object detection algorithm. The Euclidean distance is calculated and compared with the standard distance given. The persons with enough distance will be indicated in the green-colored bounding box and others with a red-colored bounding box. The total number of violations is also shown at the output feed. This project provides a solution to eradicate the spread of the deadly virus to an extent with minimum human effort and reduced risk of infection to the local athourities.

Accuracy and Efficiency Comparison of Object Detection Open-Source Models

In agriculture, weeds cause direct damage to the crop, and it primarily affects the crop yield potential. Manual and mechanical weeding methods consume a lot of energy and time and do not give efficient results. Chemical weed control is still the best way to control weeds. However, the widespread and large-scale use of herbicides is harmful to the environment. Our study's objective is to propose an efficient model for a smart system to detect weeds in crops in real-time using computer vision. Our experiment dataset contains images of two different weed species well known in our region strained in this region with a temperate climate. The first is the Phalaris Paradoxa. The second is Convolvulus, manually captured with a professional camera from fields under different lighting conditions (from morning to afternoon in sunny and cloudy weather). The detection of weed and crop has experimented with four recent pre-configured open-source computer vision models for object detection: Detectron2, EfficientDet, YOLO, and Faster R-CNN. The performance comparison of weed detection models is executed on the Open CV and Keras platform using python language.

Computer vision reimages rigor of APPT pain site and surface area measurement

The Adolescent Pediatric Pain Tool (APPT) contains a body outline diagram (BOD) to facilitate self-report of pain sites and surface area. Inter-rater reliability for manually coded BOD drawings is only fair to moderate. Irregular drawings are prone to manual measurement error. The clinical utility, trending, and interpretability of BOD drawings are significant limitations of APPT. A rigorous method to code BODs and extract data for further analysis is needed. Aim: To digitize BOD drawings and using computer vision improve accuracy of BOD measures to facilitate further analysis. Open source computer vision library, OpenCV, was used to digitize BODs from adolescents with scoliosis (n=42) or pectus excavatum (n=24) obtained preoperatively, daily postoperatively during hospitalization, and at postoperative visits. Pain sites and areas were drawn by adolescents on paper BODs, then scanned and cleaned through grayscaling, median blurring, and thresholding techniques to reduce noise. Scanned images were aligned with reference diagram using homography matrix. Differences between images and reference were extracted. Identified body parts and surface areas were labeled for further analysis. The model was able to extract BOD drawings and identify pain sites that corresponded to APPT scoring template. Correct classification rate for each body location for each patient was 97.2%. Overall sensitivity was 95.5% and specificity 97.3%. This computer vision method of coding BODs is accessible, convenient, and provides better sensitivity and specificity for pain site and surface area than manual coding. Extracted data can be further analyzed for trends and modeled for pain trajectory prediction. While the use of digital applications to prospectively collect BOD data may overcome current APPT limitations, loss of historic data, expense, data access, and electronic medical record (EMR) integration present new limitations. Paper BODs are convenient, inexpensive, easily scanned into EMRs, and now easily analyzed.

A Tree Ring Measurement Method Based on Error Correction in Digital Image of Stem Analysis Disk

Stem analysis is an essential aspect in forestry investigation and forest management, as it is a primary method to study the growth law of trees. Stem analysis requires measuring the width and number of tree rings to ensure the accurate measurement, expand applicable tree species, and reduce operation cost. This study explores the use of Open Source Computer Vision Library (Open CV) to measure the ring radius of analytic wood disk digital images, and establish a regression equation of ring radius based on image geometric distortion correction. Here, a digital camera was used to photograph the stem disks’ tree rings to obtain digital images. The images were preprocessed with Open CV to measure the disk’s annual ring radius. The error correction model based on the least-square polynomial fitting method was established for digital image geometric distortion correction. Finally, a regression equation for tree ring radius based on the error correction model was established. Through the above steps, click the intersection point between the radius line and each ring to get the pixel distance from the ring to the pith, then the size of ring radius can be calculated by the regression equation of ring radius. The study’s method was used to measure the digital image of the Chinese fir stem disk and compare it with the actual value. The results showed that the maximum error of this method was 0.15 cm, the average error was 0.04 cm, and the average detection accuracy reached 99.34%, which met the requirements for measuring the tree ring radius by stem disk analysis. This method is simple, accurate, and suitable for coniferous and broad-leaved species, which allows researchers to analyze tree ring radius measurement, and is of great significance for analyzing the tree growth process.

Geleneksel Kuru Kaymakların Bazı Kimyasal, Tekstürel, Mikrobiyolojik ve Duyusal Özellikleri

Kuru kaymak, geleneksel kaymağın tandırda ve/veya gölgede kurutulması ile üretilen bir süt ürünüdür. Bu çalışmada, Erzurum ve Nevşehir piyasalarından toplanan Kuru kaymak örneklerinde (10 adet) bazı fiziksel, kimyasal, tekstürel, mikrobiyolojik ve duyusal özelliklerin belirlenmesi amaçlanmıştır. Ayrıca örneklerin görüntüleri, OpenCV (Open Source Computer Vision Library) görüntü işleme kütüphanesi ve C++ dili kullanılarak analiz edilmiştir. Kuru kaymak örneklerinde laktik asit (%) değerinin %0,46-1,21 aralığında ve pH değerinin 6,19-6,83 aralığında değiştiği saptanmıştır. Örneklerin ortalama kurumadde, yağ ve kül içerikleri sırasıyla %71,44, %42,25 ve %2,70 olarak tespit edilmiştir. Yapılan renk ölçümleri ile ortalama L* değerinin 77,42, a* değerinin 2,13 ve b* değerinin 17,44 olduğu belirlenmiştir. Kuru kaymak örneklerinin ortalama sertlik değerinin 1761,37 N, esneklik değerinin 4,30, iç yapışkanlık değerinin 0,64, sakızımsılık değerinin 689,41 N, çiğnenebilirlik değerinin 1212,00 N ve elastikiyet değerinin 0,60 olduğu tespit edilmiştir. Görüntü işleme analizi sonucuna göre örneklerin gözeneklilik durumu %12,27-29,84 aralığında değişmektedir. Mikrobiyolojik değerlendirme sonucunda örneklerde ortalama 3,94 log kob/g maya-küf ve 3,97 log kob/g koliform grubu bakterilerin bulunduğu belirlenmiştir. Ayrıca 4 örnekte Escherichia coli tespit edilmiştir. Görünüş, yapı ve koku parametreleri örneklerde sırasıyla ortalama 5,77, 5,75 ve 5,49 olarak tespit edilmiştir. Bu çalışmada, birbirinden farklı özelliklerde Kuru kaymak örneklerinin olduğu anlaşılmaktadır. Unutulmak üzere olan bu geleneksel süt ürününün özelliklerinin optimize edilmesi, standart ve hijyenik koşullarda üretilmesinin gerekli olduğu düşünülmektedir.

Quantifying Optically Derived Two-Dimensional Wave-Averaged Currents in the Surf Zone

Complex two-dimensional nearshore current patterns are generated by feedbacks between sub-aqueous morphology and momentum imparted on the water column by breaking waves, winds, and tides. These non-stationary features, such as rip currents and circulation cells, respond to changing environmental conditions and underlying morphology. However, using fixed instruments to observe nearshore currents is limiting due to the high costs and logistics necessary to achieve adequate spatial sampling resolution. A new technique for processing surf-zone imagery, WAMFlow, quantifies fluid velocities to reveal complex, multi-scale (10 s–1000 s meters) nearshore surface circulation patterns. We apply the concept of a wave-averaged movie (WAM) to measure surf-zone circulation patterns on spatial scales of kilometers in the alongshore and 100 s of meters in the cross-shore. The approach uses a rolling average of 2 Hz optical imagery, removing the dominant optical clutter of incident waves, to leave the residual foam or water turbidity features carried by the flow. These residual features are tracked as quasi-passive tracers in space and time using optical flow, which solves for u and v as a function of image intensity gradients in x, y, and t. Surf zone drifters were deployed over multiple days with varying nearshore circulations to validate the optically derived flow patterns. Root mean square error are reduced to 0.1 m per second after filtering based on image attributes. The optically derived patterns captured longshore currents, rip currents, and gyres within the surf zone. Quantifying nearshore circulation patterns using low-cost image platforms and open-source computer vision algorithms presents the potential to further our understanding of fundamental surf zone dynamics.

Online Attendance Management System Based on Face Recognition Using CNN

Due to the growing trend of online classes, there is a pressing need to create an efficient system that can keep track of the students’ regular presence in these lectures. The traditional method of manual calculation of attendance proves to be ineffective and time-consuming. Hence, this problem can be addressed by building a system which will perform the task of marking the attendance of students by matching their faces in the multiple frames of an online class with their respective images present in the database. In this paper, face detection and recognition is being done on a video given as input to our program. Multiple faces are detected and recognised simultaneously in a class lecture. Face detection and recognition application is implemented using an open source computer vision called OpenCV. The paper doesn’t venture into real time application though it has a future in real time detection. The essential algorithms that have been extensively used are Convolution Neural Network and VGG16 architecture along with Haar features. Moreover, significant changes are to be integrated in order to bring an upheaval in the major image analysis modules such that the framework incorporates accuracy and robustness even when other variations on poses are imposed. Our framework has been evolved to detect and recognise faces in fixed time periods and mark the student’s attendance according to the average value of the faces detected and recognised within those fixed time periods such that the online attendance system can’t be evaded or cheated.

Design and analysis of gantry robot for pick and place mechanism with Arduino Mega 2560 microcontroller and processed using pythons

Abstract Robots are extensively used in industries for their precision work and amount of work that one can obtain without any defects. In this paper we are using a gantry robot for as it does not occupy the floor space therefore reducing the distance for reachability of the parts and hence reducing unnecessary material for guide way. Robots work in strictly defined path and there is no or very little change in such systems in order to overcome this we are using a vision based control system to make the system dynamic in nature the images are picked by using a USB camera processed images of the object is transmitted via serial communication to the Arduino Mega 2560 microcontroller and processed using pythons open source computer vision (Open CV) image to process the image captured by the USB camera to find the exact colour and to pick the object and sort it.

Facial Emotion Recognition In Real-time Using Deep Convolution Networks

The objective of this study is to present a detection method associated to automatic live facial expression identification by analysing the frontal-face image and predicting the most accurate expression out of the seven major countenances. Since our behaviour is strongly correlated to our emotions, the facial expressions and body gestures may act as a noteworthy source of non-verbal communication that may tell about the state of an individual. The interest in emotional computing through facial expressions has increased as it has wide application in industries, market and medical field. The doctors may be helped through facial expression recognition, may be by online machine monitoring system or sometimes when any patient is not able to communicate verbally. This study presents a model for facial recognition that permits disturbances to apprehend information from their surroundings in real-time, thus improving the classification process. The work has been implemented using Python IDLE (3.7) and Open Source Computer Vision Library (Open-CV2). The study proves to provide accurate and precise results.

Face recognition using viola-jones depending on python

<p>In this paper, the proposed software system based on face recognition the proposed system can be implemented in the smart building or any VIP building need security interring in general, The human face will be recognized from a stream of pictures or video feed, this technology recognizes the person according to the specific algorithm, the algorithm that employed in this paper is the Viola–Jones object detection framework by using Python. The task of the proposed facial recognition system consists of two steps, the first one was detected the human face from live video using the webcamera in the computer, and the second step recognizes if this face allowed to enter the building or not by comparing it with the existing database, the two steps depending on the OpenCV python by importing cv2 method for detecting the human face, the frames can be read or written to file with the cv2.imread and cv2.imwrite functions respectively Finally, this proposed software system can be used to control access in smart buildings as a rule and the advancement of techniques connected around there, Providing a security system is one of the most important features must be achieved in the smart buildings, this proposed system can be used as an application in a smart building as a security system. Face recognition is one of the most important applications using today for practical facial recognition, The proposed software system, depending on using OpenCV (Open Source Computer Vision) is a popular computer vision library, in 1999 this library started by Intel. The platform library sets its focus on real-time image processing and includes patent-free implementations of the latest computer vision algorithms. OpenCV 2.3.1 now comes with a programming interface to C, C++, Python, and Android. OpenCV library of python, the three algorithms that will be used in this proposed system. The currently available algorithms are:</p><p>Eigenfaces → createEigenFaceRecognizer()</p><p>Fisherfaces → createFisherFaceRecognizer()</p><p>Local Binary Patterns Histograms → createLBPHFaceRecognizer()</p>Finally the proposed system provide entering to the building just for the authorized person according to face recognition algorithem.<p> </p>

STML (Sketch to Markup Language)

“Web development encompasses many stages, which involve designing the website done by making a wireframe first, after which the code for the frontend and backend is appropriately written over many iterations until the developers arrive at a fully functional and satisfying website for their need. We see how every website starts with the basic idea and structure of the website that can be described using a wireframe. It shows the basic elements and gives developers an idea about the structure of the website. It is given to the developers to create the boilerplate code that places all the elements into place accordingly. The task of converting the wireframe to HTML code is tedious and time-consuming. At present, the boilerplate code is mostly written manually. Currently, the users have to write the HTML code for structuring the elements on the webpage, which leads to a lot of redundant work and users investing their precious time into it. Generally, where the structure of web pages is the same, users would tend to maintain a copy of the pre-existing boilerplate code for reusability. In most cases, the boilerplate code differs for web pages despite the code for the HTML elements being the same. In such situations, the work becomes redundant. However, this process can be automated to ease the world of the web developer community. To make this process effortless, we propose a machine learning model. It will be trained to identify specific symbols and shapes as elements, recognize texts from the wireframe. The model will take in wireframe images as input through the web application. The idea is to process input, recognize each element in the wireframe using the Open Source Computer vision library (OpenCV). Identified elements will each have a corresponding code in the backend. Once the elements on the wireframe are identified, the corresponding code is written into an HTML file. The user gets the HTML file as an output. The creation of the boilerplate code for a website becomes much less time-consuming, which gives developers the freedom to test out many different designs and layouts before opting for the one that best suits their needs.”

Computer vision supported pedestrian tracking: A demonstration on trail bridges in rural Rwanda.

Trail bridges can improve access to critical services such as health care, schools, and markets. In order to evaluate the impact of trail bridges in rural Rwanda, it is helpful to objectively know how and when they are being used. In this study, we deployed motion-activated digital cameras across several trail bridges installed by the non-profit Bridges to Prosperity. We conducted and validated manual counting of bridge use to establish a ground truth. We adapted an open source computer vision algorithm to identify and count bridge use reflected in the digital images. We found a reliable correlation with less than 3% error bias of bridge crossings per hour between manual counting and those sites at which the cameras logged short video clips. We applied this algorithm across 186 total days of observation at four sites in fall 2019, and observed a total of 33,800 daily bridge crossings ranging from about 20 to over 1,100 individual uses per day, with no apparent correlation between daily or total weekly rainfall and bridge use, potentially indicating that transportation behaviors, after a bridge is installed, are no longer impacted by rainfall conditions. Higher bridge use was observed in the late afternoons, on market and church days, and roughly equal use of the bridge crossings in each direction. These trends are consistent with the design-intent of these bridges.

Whole-Brain Image Analysis and Anatomical Atlas 3D Generation Using MagellanMapper.

MagellanMapper is a software suite designed for visual inspection and end-to-end automated processing of large-volume, 3D brain imaging datasets in a memory-efficient manner. The rapidly growing number of large-volume, high-resolution datasets necessitates visualization of raw data at both macro- and microscopic levels to assess the quality of data, as well as automated processing to quantify data in an unbiased manner for comparison across a large number of samples. To facilitate these analyses, MagellanMapper provides both a graphical user interface for manual inspection and a command-line interface for automated image processing. At the macroscopic level, the graphical interface allows researchers to view full volumetric images simultaneously in each dimension and to annotate anatomical label placements. At the microscopic level, researchers can inspect regions of interest at high resolution to build ground truth data of cellular locations such as nuclei positions. Using the command-line interface, researchers can automate cell detection across volumetric images, refine anatomical atlas labels to fit underlying histology, register these atlases to sample images, and perform statistical analyses by anatomical region. MagellanMapper leverages established open-source computer vision libraries and is itself open source and freely available for download and extension. © 2020 Wiley Periodicals LLC. Basic Protocol 1: MagellanMapper installation Alternate Protocol: Alternative methods for MagellanMapper installation Basic Protocol 2: Import image files into MagellanMapper Basic Protocol 3: Region of interest visualization and annotation Basic Protocol 4: Explore an atlas along all three dimensions and register to a sample brain Basic Protocol 5: Automated 3D anatomical atlas construction Basic Protocol 6: Whole-tissue cell detection and quantification by anatomical label Support Protocol: Import a tiled microscopy image in proprietary format into MagellanMapper.

Analyzing the Impact of Iridescence Strength on Animal Shape Recognition Using Computer Vision

Iridescence, commonly found in nature, is the quality of a material to change colors depending on its interaction with light. For example, certain species of beetles distract predators with their iridescent exoskeletons that flash different colors depending on the angle the predator looks at them. Iridescent flowers, however, attract pollinators through their noticeable changes in color. Thus, it seems that iridescence can serve two contrasting purposes: concealment and attraction. The purpose of this project is to test whether different strengths in iridescence can impact its recognizability to animals and insects. 
 Three different iridescent diffraction grating films were experimented with: 500 lines/mm, 532 lines/mm, and 1000 lines/mm. Matte paper was used as a control. Four circular epoxy resin disks (8 cm diameter) and four square epoxy resin disks (7 cm side length) were created. Each diffraction grating film type and the matte paper was glued onto one disk of each shape. Photos of each disk were then taken at angles of 30, 45, 60, and 90 degrees in a photo booth and processed through a computer vision algorithm, the Ramer-Douglas-Peucker algorithm, to perform contour approximation.
 The results gathered cannot conclude that the strengths of iridescence have an impact on shape recognition and detectability due to limitations and potential errors. Nonetheless, the study can suggest that the specific usage of the Ramer-Douglas-Peucker algorithm for edge and area detection through OpenCV (an open-source computer vision library) is inadequate in imitating animal and insect visual systems when analyzing iridescence.

Precipitating Cloud Analysis during Extreme Rain Events over the Chao Phraya River Basin Based on the Developed Radar Mosaic Products

During rainy season, Tropical Cyclones (TCs) regularly provide heavy rain over Indochina. In addition, the monsoon trough also produces rainfall to support agricultural activities across the area. However, the extreme rainfall events usually occur during the seasonal monsoon march. Understanding the precipitating cloud characteristics produced by the extreme rainfall events could alleviate the destruction of natural resources over Indochina. The variation in precipitating clouds can be analyzed using fine spatio-temporal resolution data, such as ground-based radar data from multiple radar stations. Since the Chao Phraya river basin is the most important basin in Thailand, this study is based on radar images collected from the five installed radar stations of the Thai Meteorological Department over this basin. The analysis of the extreme rain events during rainy season in 2018 was carried out using the developed mosaic method. Python and Open Source Computer Vision Library (OpenCV) were used in the development of mosaic products to focus on characteristics of precipitating clouds. Beam-blockage fraction was analyzed for each radar station and surrounding terrain using an open source library for processing weather radar data (wradlib) (Heistermann et al., 2013) with our developed beam-blockage analysis program. The severity of blocked beam is found over the northern mountain region of study area. The precipitating clouds were detected and their parameters extracted using the ellipse fitting technique. The analysis of these clouds revealed that, among other cases, Sontihn TC was a severely destructive event based on the storm parameters of both stratiform and convective clouds. The number and length of the convective storms during Sontihn TC was the most severe when compared to other events. It has been found that the diurnal cycle of the convective cloud shows their patterns by following solar heating surface over the study area for all of the selected events.

Computer Vision Analysis of Reduced Interpersonal Affect Coordination in Youth With Autism Spectrum Disorder.

Atypical social-emotional reciprocity is a core feature of autism spectrum disorder (ASD) but can be difficult to operationalize. One measurable manifestation of reciprocity may be interpersonal coordination, the tendency to align the form and timing of one's behaviors (including facial affect) with others. Interpersonal affect coordination facilitates sharing and understanding of emotional cues, and there is evidence that it is reduced in ASD. However, most research has not measured this process in true social contexts, due in part to a lack of tools for measuring dynamic facial expressions over the course of an interaction. Automated facial analysis via computer vision provides an efficient, granular, objective method for measuring naturally occurring facial affect and coordination. Youth with ASD and matched typically developing youth participated in cooperative conversations with their mothers and unfamiliar adults. Time-synchronized videos were analyzed with an open-source computer vision toolkit for automated facial analysis, for the presence and intensity of facial movements associated with positive affect. Both youth and adult conversation partners exhibited less positive affect during conversations when the youth partner had ASD. Youth with ASD also engaged in less affect coordination over the course of conversations. When considered dimensionally across youth with and without ASD, affect coordination significantly predicted scores on rating scales of autism-related social atypicality, adaptive social skills, and empathy. Findings suggest that affect coordination is an important interpersonal process with implications for broader social-emotional functioning. This preliminary study introduces a promising novel method for quantifying moment-to-moment facial expression and emotional reciprocity during natural interactions. LAY SUMMARY: This study introduces a novel, automated method for measuring social-emotional reciprocity during natural conversations, which may improve assessment of this core autism diagnostic behavior. We used computerized methods to measure facial affect and the degree of affect coordination between conversation partners. Youth with autism displayed reduced affect coordination, and reduced affect coordination predicted lower scores on measures of broader social-emotional skills.

Generalized Omnipresence Detection

Identification and Tracking of a person in a video are useful in applications such as video surveillance. Two levels of tracking are carried out. They are Classification and monitoring of individuals. The human body’s color histogram is used as the basis for monitoring individuals. Our project can detect a human face in a video and store the detected facial features of the Local Binary Pattern Histogram (LBPH). In a video, once a person is detected, it automatically track that individual and assigns a label to that individual. We use the stored LBPH features to track him in any other videos. In this paper, we proposed and compared the efficiency of two algorithms. One constantly updates the background to make it suitable for illumination changes and other uses depth information with RGB. This is the first step in many complex algorithms in computer vision, such as identification of human activity and behavior recognition. The main challenges in human/object detection and tracking are changing illumination and background. Our work is based on image processing and also it learns the activities and stores them using machine learning with the help of OpenCV, an open source computer vision library.

GPU-Accelerated Vision for Robots: Improving System Throughput Using OpenCV and CUDA

OpenCV is an open source computer vision and machine learning library for C/C++/Python available for Windows, Linux, macOS, and Android platforms. It contains low-level image processing functions as well as high-level algorithms such as object identification, face recognition, and action classification in videos. OpenCV has become very popular, with more than 47,000 people in its user community and 18 million downloads (see https://opencv.org/about/). Under a Berkeley Software Distribution (BSD) license, it can be used for both academic and commercial applications.